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Title: Advancing Coal Catalytic Gasification to Promote Optimum Syngas Production

Abstract

An investigation was pursued to improve the use of low rank sub-bituminous coal by using suitable catalysts to advance gasification and create cleaner synthesis gas. Bench- and pilot-scale experiments were performed to measure key reactive properties and verify the use of red mud catalyst. The experiments provided reaction data for the development of kinetic models and validation data for CFD simulations. The reaction kinetics were analyzed to provide an understanding of the major pathways involved and identify the necessary reaction mechanisms needed for the computational models. The reaction kinetics model was implemented into the computational fluid dynamics (CFD) code MFiX to simulate, predict and validate the chemistry model with the experiments. It was shown that red mud as a catalyst lowered methane formation during coal gasification. Additional benefits were that red mud destroyed tars formation, promoted water gas shift reaction resulting in a syngas rich in hydrogen. In the presence of red mud catalyst, biomass feedstocks high in ash did not form clinker in the fluidized bed because it prevented the formation of eutectic mixtures. The reaction kinetics model was reasonably robust and the CFD predictions using the model were in good agreement with the experiments.

Authors:
 [1];  [1];  [1];  [1]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Publication Date:
Research Org.:
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1444101
Report Number(s):
DOE-VT-FE-0024013
DOE Contract Number:  
FE0024013
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 01 COAL, LIGNITE, AND PEAT; red mud catalyst; fluidized beds; reaction kinetics

Citation Formats

Battaglia, Francine, Agblevor, Foster, Klein, Michael, and Sheikhi, Reza. Advancing Coal Catalytic Gasification to Promote Optimum Syngas Production. United States: N. p., 2017. Web. doi:10.2172/1444101.
Battaglia, Francine, Agblevor, Foster, Klein, Michael, & Sheikhi, Reza. Advancing Coal Catalytic Gasification to Promote Optimum Syngas Production. United States. doi:10.2172/1444101.
Battaglia, Francine, Agblevor, Foster, Klein, Michael, and Sheikhi, Reza. Tue . "Advancing Coal Catalytic Gasification to Promote Optimum Syngas Production". United States. doi:10.2172/1444101. https://www.osti.gov/servlets/purl/1444101.
@article{osti_1444101,
title = {Advancing Coal Catalytic Gasification to Promote Optimum Syngas Production},
author = {Battaglia, Francine and Agblevor, Foster and Klein, Michael and Sheikhi, Reza},
abstractNote = {An investigation was pursued to improve the use of low rank sub-bituminous coal by using suitable catalysts to advance gasification and create cleaner synthesis gas. Bench- and pilot-scale experiments were performed to measure key reactive properties and verify the use of red mud catalyst. The experiments provided reaction data for the development of kinetic models and validation data for CFD simulations. The reaction kinetics were analyzed to provide an understanding of the major pathways involved and identify the necessary reaction mechanisms needed for the computational models. The reaction kinetics model was implemented into the computational fluid dynamics (CFD) code MFiX to simulate, predict and validate the chemistry model with the experiments. It was shown that red mud as a catalyst lowered methane formation during coal gasification. Additional benefits were that red mud destroyed tars formation, promoted water gas shift reaction resulting in a syngas rich in hydrogen. In the presence of red mud catalyst, biomass feedstocks high in ash did not form clinker in the fluidized bed because it prevented the formation of eutectic mixtures. The reaction kinetics model was reasonably robust and the CFD predictions using the model were in good agreement with the experiments.},
doi = {10.2172/1444101},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {10}
}